In the past few decades, the cost of a college education has increased exponentially, leaving students buried under debt and the loan industry simultaneously predatory and wary. What’s more, due to a greater number of students attending college and federal loan initiatives that set eligibility rates based on income, even students who are unlikely to graduate or be able to repay their loans qualify during their initial period of study.
At present, about 11 percent of students default on loans, and this isn’t including loan deferral programs and other delayed payment options that allow recent grads to get on their feet before they start paying them back. Without these programs, the default rate would undoubtedly be higher, but 11 percent is still unacceptably high and is putting students’ financial futures at risk. By turning to big data, can we bring this rate down and solve the student loan crisis?

Spot High Risk Loans

The primary way that big data can help lenders and students reduce high risk lending and defaults is by developing a system driven by risk analysis. For example, a significant proportion of students who default on their loans are concentrated at a subgroup of colleges – schools with low graduation rates that fail to prepare students for future employment.
With that data in hand, there’s a simple solution – and more than one politician has suggested it might be wise: if these schools are required to raise standards before their students are eligible for loans, low performing schools would either go under or improve. Either way, they’d be doing a service to students who, right now, are just being cheated out of education and money. Students shouldn’t come away with six figures of loan debt and no degree or job prospects.

Get With The Program

Another factor that lenders should take into consideration when offering student loans is what big data has to say about repayment programs. According to the CFPB, too few graduates are enrolled in income-based repayment programs, even after repairing a default situation. These programs scale the amount a borrower is expected to pay based on their current income.
To prevent defaults, more lenders should be pushing these programs, or even automating enrollment, even though it can slow loan repayment. Slow payment is better than no payment.
For those lenders that don’t offer such programs, another option to reduce defaults is to identify vulnerable borrowers and encourage them to enroll in a student loan consolidation program. If students have borrowed from more than one source or are trapped with a high interest rate, these programs can help make loan debt more manageable.

Play Big Brother

Ultimately, using big data to end the student lending crisis demands that lenders play big brother to a certain degree. That means determining which students are most likely to default by performing a historical survey that analyzes data like standardized test scores, enrollment status, and alumni association involvement, and then applying that data to the current pool of borrowers.
Similarly, a short survey a few weeks into the semester can often determine semester end grade outcomes – that’s the kind of information that should inform initial lending. You don’t want to lend to failing students.
Modern lenders know that FICO scores don’t tell the whole story and they’re looking more closely at borrowers; smart student loan providers should be following a similar trail. Big data can tell you who you’re lending to and whether or not they’ll ever be likely to pay that money back.
Protect your assets and protect borrowers’ financial futures – lend smarter with big data.